1. Field of the Invention
This application relates to a weaning readiness indicator, a sleeping status recording device, and an air providing system, and more particularly, to a weaning readiness indicator, a sleeping status recording device, and an air providing system applying nonlinear time-frequency analysis (NTFA).
2. Description of the Related Art
In medical procedures for treating a patient's health issue, the first step is to obtain the biological data of the patient. In addition to the general information such as height, age, or gender, the most common examinations are blood test, imaging information, nerve conduction examination, etc., which include invasive and non-invasive examinations. Each examination has its different values and necessities under different circumstances and different timings. In recent decades, the dynamic information of physiological signals, such as heart rate variability (HRV) and breathing rate variability (BRV), have become a study of interest. Many doctors and researchers are keen on understanding the dynamic information of physiological signals to further improve medical treatments.
Since the invasive examination is not conducive for the long time data collection, we are focusing on the non-invasive examinations first before the invention of the simplified invasive examinations. The common non-invasive examinations include electrocardiography (ECG), respiratory signals, medical imaging, etc. However, medical imaging requires a high cost, and is limited to describe the dynamic variation over a short period of time. On the other hand, some data such as time-varying frequency and amplitude of the ECG signals or the respiratory signals are important subjects of great interests to many scientists. According to the recent researches, the HRV and the BRV may contain a lot of precious physiological information. However, even with lots of efforts over many years, the clinical values of such signals are still limited.
Examples of dynamical information with clinical value extracted from the physiological signal include time-varying frequency and amplitude. In general, such time-varying frequency is not measured directly, but is inferred from the temporally oscillatory signals. A well-known example is the analysis of R-peak to R-peak intervals (RRI) to reveal HRV information. To study the extracted time-very frequency signal, many techniques are introduced, including, for example, spectral method and nonlinear dynamical analysis, such as Poincare map, entropy analysis, fractal analysis.
However, there are some of the following limitations with these established analysis techniques. Firstly, a relatively large number of oscillations must be observed in the physiologic signal. For example, when applying Poincare map or approximate entropy analysis on the respiratory signal, at least 300 and 100 to 1000 oscillations are needed respectively. Second of all, it is not always straightforward to determine the oscillation-to-oscillation time series from the given physiological signals, as there is no reliable determination of the “true” landmarks that can be guaranteed. Furthermore, in some cases, it is even hard to provide a universally accepted definition of a landmark (e.g., ECG signals). Third, the information will be over-reduced inside the physiological signal if what is retained is only the oscillation-to-oscillation time series.